Overview

Dataset statistics

Number of variables42
Number of observations204
Missing cells285
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.1 KiB
Average record size in memory336.6 B

Variable types

Numeric30
Categorical12

Alerts

COST_DEVIATION is highly correlated with ADDITIONAL_COSTHigh correlation
TIME_DEVIATION is highly correlated with ADDITIONAL_TIMEHigh correlation
ADVANCED_PAYMENT is highly correlated with K_CONTRACTING_B_VALUEHigh correlation
ESTIMATED_COST is highly correlated with PROJECT_INTENSITY and 3 other fieldsHigh correlation
ORIGINAL_DEADLINE is highly correlated with FINAL_DEADLINEHigh correlation
LIQUIDITY_INDEX_B is highly correlated with DEBT_INDEX_B and 2 other fieldsHigh correlation
DEBT_INDEX_B is highly correlated with LIQUIDITY_INDEX_B and 2 other fieldsHigh correlation
INTEREST_COVERAGE_RATIO_B is highly correlated with LIQUIDITY_INDEX_B and 3 other fieldsHigh correlation
ROE_B is highly correlated with INTEREST_COVERAGE_RATIO_B and 2 other fieldsHigh correlation
ROA_B is highly correlated with LIQUIDITY_INDEX_B and 3 other fieldsHigh correlation
WORKING_CAPITAL is highly correlated with NET_EQUITYHigh correlation
NET_EQUITY is highly correlated with ROE_B and 1 other fieldsHigh correlation
K_CONTRACTING_B_VALUE is highly correlated with ADVANCED_PAYMENTHigh correlation
PROJECT_INTENSITY is highly correlated with ESTIMATED_COST and 2 other fieldsHigh correlation
PRICE_SCORE is highly correlated with TECHNICAL_SCOREHigh correlation
TECHNICAL_SCORE is highly correlated with PRICE_SCOREHigh correlation
CONTRACT_VALUE is highly correlated with ESTIMATED_COST and 3 other fieldsHigh correlation
ADDITIONAL_COST is highly correlated with COST_DEVIATIONHigh correlation
FINAL_COST is highly correlated with ESTIMATED_COST and 3 other fieldsHigh correlation
ADDITIONAL_TIME is highly correlated with TIME_DEVIATION and 1 other fieldsHigh correlation
FINAL_DEADLINE is highly correlated with ESTIMATED_COST and 4 other fieldsHigh correlation
COST_DEVIATION is highly correlated with ADDITIONAL_COSTHigh correlation
TIME_DEVIATION is highly correlated with PROJECT_INTENSITY and 1 other fieldsHigh correlation
ESTIMATED_COST is highly correlated with PROJECT_INTENSITY and 2 other fieldsHigh correlation
ORIGINAL_DEADLINE is highly correlated with FINAL_DEADLINEHigh correlation
ROE_B is highly correlated with ROA_BHigh correlation
ROA_B is highly correlated with ROE_BHigh correlation
WORKING_CAPITAL is highly correlated with NET_EQUITYHigh correlation
NET_EQUITY is highly correlated with WORKING_CAPITALHigh correlation
PROJECT_INTENSITY is highly correlated with TIME_DEVIATION and 4 other fieldsHigh correlation
PRICE_SCORE is highly correlated with TECHNICAL_SCOREHigh correlation
TECHNICAL_SCORE is highly correlated with PRICE_SCOREHigh correlation
NUMBER_BIDDERS is highly correlated with PROJECT_INTENSITYHigh correlation
CONTRACT_VALUE is highly correlated with ESTIMATED_COST and 2 other fieldsHigh correlation
ADDITIONAL_COST is highly correlated with COST_DEVIATIONHigh correlation
FINAL_COST is highly correlated with ESTIMATED_COST and 2 other fieldsHigh correlation
ADDITIONAL_TIME is highly correlated with TIME_DEVIATION and 1 other fieldsHigh correlation
FINAL_DEADLINE is highly correlated with ORIGINAL_DEADLINE and 1 other fieldsHigh correlation
COST_DEVIATION is highly correlated with ADDITIONAL_COSTHigh correlation
TIME_DEVIATION is highly correlated with ADDITIONAL_TIMEHigh correlation
ADVANCED_PAYMENT is highly correlated with K_CONTRACTING_B_VALUEHigh correlation
ESTIMATED_COST is highly correlated with PROJECT_INTENSITY and 2 other fieldsHigh correlation
ORIGINAL_DEADLINE is highly correlated with FINAL_DEADLINEHigh correlation
LIQUIDITY_INDEX_B is highly correlated with DEBT_INDEX_BHigh correlation
DEBT_INDEX_B is highly correlated with LIQUIDITY_INDEX_BHigh correlation
ROE_B is highly correlated with ROA_BHigh correlation
ROA_B is highly correlated with ROE_BHigh correlation
WORKING_CAPITAL is highly correlated with NET_EQUITYHigh correlation
NET_EQUITY is highly correlated with WORKING_CAPITALHigh correlation
K_CONTRACTING_B_VALUE is highly correlated with ADVANCED_PAYMENTHigh correlation
PROJECT_INTENSITY is highly correlated with ESTIMATED_COST and 2 other fieldsHigh correlation
PRICE_SCORE is highly correlated with TECHNICAL_SCOREHigh correlation
TECHNICAL_SCORE is highly correlated with PRICE_SCOREHigh correlation
CONTRACT_VALUE is highly correlated with ESTIMATED_COST and 2 other fieldsHigh correlation
ADDITIONAL_COST is highly correlated with COST_DEVIATIONHigh correlation
FINAL_COST is highly correlated with ESTIMATED_COST and 2 other fieldsHigh correlation
ADDITIONAL_TIME is highly correlated with TIME_DEVIATIONHigh correlation
FINAL_DEADLINE is highly correlated with ORIGINAL_DEADLINEHigh correlation
DEPARTMENT is highly correlated with REGION and 1 other fieldsHigh correlation
REGION is highly correlated with DEPARTMENT and 1 other fieldsHigh correlation
REGION_2 is highly correlated with DEPARTMENT and 1 other fieldsHigh correlation
NUMBER_OF_CONTRACTS is highly correlated with YEARHigh correlation
COST_DEVIATION is highly correlated with LIQUIDITY_INDEX_B and 3 other fieldsHigh correlation
TIME_DEVIATION is highly correlated with ADVANCED_PAYMENT and 10 other fieldsHigh correlation
ADVANCED_PAYMENT is highly correlated with TIME_DEVIATION and 7 other fieldsHigh correlation
ESTIMATED_COST is highly correlated with TIME_DEVIATION and 11 other fieldsHigh correlation
ORIGINAL_DEADLINE is highly correlated with ESTIMATED_COST and 5 other fieldsHigh correlation
LIQUIDITY_INDEX_B is highly correlated with COST_DEVIATION and 2 other fieldsHigh correlation
INTEREST_COVERAGE_RATIO_B is highly correlated with COST_DEVIATION and 3 other fieldsHigh correlation
ROE_B is highly correlated with INTEREST_COVERAGE_RATIO_B and 2 other fieldsHigh correlation
ROA_B is highly correlated with INTEREST_COVERAGE_RATIO_B and 1 other fieldsHigh correlation
WORKING_CAPITAL is highly correlated with ORIGINAL_DEADLINE and 3 other fieldsHigh correlation
NET_EQUITY is highly correlated with ORIGINAL_DEADLINE and 4 other fieldsHigh correlation
EXPERIENCE_B_VALUE is highly correlated with TIME_DEVIATION and 8 other fieldsHigh correlation
K_CONTRACTING_B_VALUE is highly correlated with NET_EQUITY and 1 other fieldsHigh correlation
PROJECT_INTENSITY is highly correlated with TIME_DEVIATION and 12 other fieldsHigh correlation
PRICE_SCORE is highly correlated with TECHNICAL_SCORE and 3 other fieldsHigh correlation
TECHNICAL_SCORE is highly correlated with EXPERIENCE_B_VALUE and 3 other fieldsHigh correlation
NATIONAL_INDUSTRY_SCORE is highly correlated with TIME_DEVIATION and 4 other fieldsHigh correlation
OTHER_SCORE is highly correlated with HIGHEST SCORE and 2 other fieldsHigh correlation
NUMBER_BIDDERS is highly correlated with TIME_DEVIATION and 6 other fieldsHigh correlation
CONTRACT_VALUE is highly correlated with TIME_DEVIATION and 11 other fieldsHigh correlation
AWARD_GROWTH is highly correlated with NUMBER_BIDDERSHigh correlation
ADDITIONAL_COST is highly correlated with COST_DEVIATION and 6 other fieldsHigh correlation
FINAL_COST is highly correlated with TIME_DEVIATION and 10 other fieldsHigh correlation
ADDITIONAL_TIME is highly correlated with TIME_DEVIATION and 11 other fieldsHigh correlation
FINAL_DEADLINE is highly correlated with ESTIMATED_COST and 5 other fieldsHigh correlation
TIME_SUSPENDED_P is highly correlated with TIME_DEVIATION and 1 other fieldsHigh correlation
IDI is highly correlated with YEAR and 3 other fieldsHigh correlation
HIGHEST SCORE is highly correlated with PRICE_SCORE and 2 other fieldsHigh correlation
LOWEST_SCORE is highly correlated with NATIONAL_INDUSTRY_SCORE and 1 other fieldsHigh correlation
YEAR is highly correlated with NUMBER_OF_CONTRACTS and 3 other fieldsHigh correlation
IDI_CAT is highly correlated with IDI and 1 other fieldsHigh correlation
MUNICIPALITY_TYPE is highly correlated with DEPARTMENT and 2 other fieldsHigh correlation
DEPARTMENT is highly correlated with TIME_DEVIATION and 18 other fieldsHigh correlation
REGION is highly correlated with DEPARTMENT and 1 other fieldsHigh correlation
REGION_2 is highly correlated with MUNICIPALITY_TYPE and 3 other fieldsHigh correlation
OWNER is highly correlated with ADVANCED_PAYMENT and 11 other fieldsHigh correlation
PERFORMANCE is highly correlated with COST_DEVIATION and 1 other fieldsHigh correlation
INTEREST_COVERAGE_RATIO_B has 6 (2.9%) missing values Missing
ROE_B has 3 (1.5%) missing values Missing
ROA_B has 3 (1.5%) missing values Missing
WORKING_CAPITAL has 109 (53.4%) missing values Missing
NET_EQUITY has 147 (72.1%) missing values Missing
EXPERIENCE_B_VALUE has 7 (3.4%) missing values Missing
K_CONTRACTING_B_VALUE has 9 (4.4%) missing values Missing
NUMBER_OF_CONTRACTS is uniformly distributed Uniform
NUMBER_OF_CONTRACTS has unique values Unique
COST_DEVIATION has 134 (65.7%) zeros Zeros
TIME_DEVIATION has 119 (58.3%) zeros Zeros
ADVANCED_PAYMENT has 144 (70.6%) zeros Zeros
INTEREST_COVERAGE_RATIO_B has 3 (1.5%) zeros Zeros
ROE_B has 10 (4.9%) zeros Zeros
ROA_B has 11 (5.4%) zeros Zeros
TECHNICAL_SCORE has 9 (4.4%) zeros Zeros
NATIONAL_INDUSTRY_SCORE has 22 (10.8%) zeros Zeros
OTHER_SCORE has 143 (70.1%) zeros Zeros
AWARD_GROWTH has 49 (24.0%) zeros Zeros
ADDITIONAL_COST has 134 (65.7%) zeros Zeros
ADDITIONAL_TIME has 119 (58.3%) zeros Zeros
TIME_SUSPENDED_P has 129 (63.2%) zeros Zeros

Reproduction

Analysis started2022-06-15 03:42:43.859877
Analysis finished2022-06-15 03:46:13.658454
Duration3 minutes and 29.8 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

NUMBER_OF_CONTRACTS
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct204
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.5
Minimum1
Maximum204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:13.860474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.15
Q151.75
median102.5
Q3153.25
95-th percentile193.85
Maximum204
Range203
Interquartile range (IQR)101.5

Descriptive statistics

Standard deviation59.03388857
Coefficient of variation (CV)0.5759403763
Kurtosis-1.2
Mean102.5
Median Absolute Deviation (MAD)51
Skewness0
Sum20910
Variance3485
MonotonicityStrictly increasing
2022-06-14T22:46:14.084556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.5%
1411
 
0.5%
1311
 
0.5%
1321
 
0.5%
1331
 
0.5%
1341
 
0.5%
1351
 
0.5%
1361
 
0.5%
1371
 
0.5%
1381
 
0.5%
Other values (194)194
95.1%
ValueCountFrequency (%)
11
0.5%
21
0.5%
31
0.5%
41
0.5%
51
0.5%
61
0.5%
71
0.5%
81
0.5%
91
0.5%
101
0.5%
ValueCountFrequency (%)
2041
0.5%
2031
0.5%
2021
0.5%
2011
0.5%
2001
0.5%
1991
0.5%
1981
0.5%
1971
0.5%
1961
0.5%
1951
0.5%

COST_DEVIATION
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct40
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08037522859
Minimum0
Maximum0.5
Zeros134
Zeros (%)65.7%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:14.308500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.07
95-th percentile0.4885
Maximum0.5
Range0.5
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.1523831655
Coefficient of variation (CV)1.895897134
Kurtosis2.071146024
Mean0.08037522859
Median Absolute Deviation (MAD)0
Skewness1.86670482
Sum16.39654663
Variance0.02322062914
MonotonicityNot monotonic
2022-06-14T22:46:14.526415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0134
65.7%
0.510
 
4.9%
0.025
 
2.5%
0.064
 
2.0%
0.044
 
2.0%
0.113
 
1.5%
0.073
 
1.5%
0.182
 
1.0%
0.092
 
1.0%
0.292
 
1.0%
Other values (30)35
 
17.2%
ValueCountFrequency (%)
0134
65.7%
0.011
 
0.5%
0.025
 
2.5%
0.031
 
0.5%
0.044
 
2.0%
0.052
 
1.0%
0.064
 
2.0%
0.064328458421
 
0.5%
0.073
 
1.5%
0.081
 
0.5%
ValueCountFrequency (%)
0.510
4.9%
0.491
 
0.5%
0.482
 
1.0%
0.47376886011
 
0.5%
0.471
 
0.5%
0.441
 
0.5%
0.431
 
0.5%
0.42844931431
 
0.5%
0.411
 
0.5%
0.41
 
0.5%

TIME_DEVIATION
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct49
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4522303922
Minimum0
Maximum21.11
Zeros119
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:14.740535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.38
95-th percentile1.5
Maximum21.11
Range21.11
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation1.785710337
Coefficient of variation (CV)3.948673879
Kurtosis99.90740031
Mean0.4522303922
Median Absolute Deviation (MAD)0
Skewness9.449312573
Sum92.255
Variance3.188761408
MonotonicityNot monotonic
2022-06-14T22:46:14.951911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0119
58.3%
0.3310
 
4.9%
0.57
 
3.4%
0.676
 
2.9%
0.255
 
2.5%
0.173
 
1.5%
1.53
 
1.5%
13
 
1.5%
0.383
 
1.5%
0.772
 
1.0%
Other values (39)43
 
21.1%
ValueCountFrequency (%)
0119
58.3%
0.071
 
0.5%
0.11
 
0.5%
0.121
 
0.5%
0.1251
 
0.5%
0.141
 
0.5%
0.161
 
0.5%
0.173
 
1.5%
0.21
 
0.5%
0.221
 
0.5%
ValueCountFrequency (%)
21.111
 
0.5%
12.91
 
0.5%
3.281
 
0.5%
3.111
 
0.5%
31
 
0.5%
2.51
 
0.5%
2.21
 
0.5%
21
 
0.5%
1.621
 
0.5%
1.53
1.5%

TIME_STUDIES_CONTRACT
Real number (ℝ≥0)

Distinct65
Distinct (%)32.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean57.64039409
Minimum16
Maximum779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:15.168170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile33
Q140.5
median49
Q360.5
95-th percentile88
Maximum779
Range763
Interquartile range (IQR)20

Descriptive statistics

Standard deviation59.49252174
Coefficient of variation (CV)1.03213246
Kurtosis113.7174187
Mean57.64039409
Median Absolute Deviation (MAD)10
Skewness9.990979502
Sum11701
Variance3539.360142
MonotonicityNot monotonic
2022-06-14T22:46:15.386969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4213
 
6.4%
399
 
4.4%
498
 
3.9%
358
 
3.9%
508
 
3.9%
437
 
3.4%
607
 
3.4%
517
 
3.4%
477
 
3.4%
387
 
3.4%
Other values (55)122
59.8%
ValueCountFrequency (%)
161
 
0.5%
202
 
1.0%
261
 
0.5%
281
 
0.5%
292
 
1.0%
301
 
0.5%
322
 
1.0%
333
 
1.5%
341
 
0.5%
358
3.9%
ValueCountFrequency (%)
7791
0.5%
4131
0.5%
1311
0.5%
1181
0.5%
1121
0.5%
1111
0.5%
1061
0.5%
971
0.5%
911
0.5%
891
0.5%

ADVANCED_PAYMENT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1185294118
Minimum0
Maximum0.5
Zeros144
Zeros (%)70.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:16.115137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3
95-th percentile0.5
Maximum0.5
Range0.5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.1939650929
Coefficient of variation (CV)1.636430064
Kurtosis-0.4589315836
Mean0.1185294118
Median Absolute Deviation (MAD)0
Skewness1.159101468
Sum24.18
Variance0.03762245726
MonotonicityNot monotonic
2022-06-14T22:46:16.307588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0144
70.6%
0.528
 
13.7%
0.413
 
6.4%
0.311
 
5.4%
0.13
 
1.5%
0.22
 
1.0%
0.352
 
1.0%
0.281
 
0.5%
ValueCountFrequency (%)
0144
70.6%
0.13
 
1.5%
0.22
 
1.0%
0.281
 
0.5%
0.311
 
5.4%
0.352
 
1.0%
0.413
 
6.4%
0.528
 
13.7%
ValueCountFrequency (%)
0.528
 
13.7%
0.413
 
6.4%
0.352
 
1.0%
0.311
 
5.4%
0.281
 
0.5%
0.22
 
1.0%
0.13
 
1.5%
0144
70.6%

ESTIMATED_COST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct194
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1207.407527
Minimum237.45
Maximum13812.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:16.499773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum237.45
5-th percentile309.3785
Q1425.5825
median649.195
Q31207.499221
95-th percentile4317.3125
Maximum13812.73
Range13575.28
Interquartile range (IQR)781.9167214

Descriptive statistics

Standard deviation1611.64858
Coefficient of variation (CV)1.334800838
Kurtosis24.31872886
Mean1207.407527
Median Absolute Deviation (MAD)269.725
Skewness4.255771888
Sum246311.1356
Variance2597411.145
MonotonicityNot monotonic
2022-06-14T22:46:16.735116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2095.135
 
2.5%
677.772
 
1.0%
310.392
 
1.0%
542.212
 
1.0%
482.632
 
1.0%
406.662
 
1.0%
519.92
 
1.0%
861.081
 
0.5%
1404.681
 
0.5%
830.561
 
0.5%
Other values (184)184
90.2%
ValueCountFrequency (%)
237.451
0.5%
277.641
0.5%
2801
0.5%
281.681
0.5%
287.41
0.5%
288.661
0.5%
291.711
0.5%
298.751
0.5%
306.131
0.5%
308.171
0.5%
ValueCountFrequency (%)
13812.731
0.5%
10302.161
0.5%
8133.21
0.5%
5819.821
0.5%
5378.1640471
0.5%
5150.281
0.5%
5144.721
0.5%
4704.561
0.5%
4574.531
0.5%
4389.131
0.5%

ORIGINAL_DEADLINE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.8578431
Minimum9
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:16.946155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile36.5
Q175
median90
Q3120
95-th percentile180
Maximum900
Range891
Interquartile range (IQR)45

Descriptive statistics

Standard deviation74.54878647
Coefficient of variation (CV)0.7042348895
Kurtosis63.75866233
Mean105.8578431
Median Absolute Deviation (MAD)30
Skewness6.425120388
Sum21595
Variance5557.521564
MonotonicityNot monotonic
2022-06-14T22:46:17.120275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
9078
38.2%
12036
17.6%
6028
 
13.7%
18012
 
5.9%
15012
 
5.9%
757
 
3.4%
306
 
2.9%
455
 
2.5%
2404
 
2.0%
502
 
1.0%
Other values (13)14
 
6.9%
ValueCountFrequency (%)
91
 
0.5%
102
 
1.0%
201
 
0.5%
306
 
2.9%
351
 
0.5%
455
 
2.5%
502
 
1.0%
6028
 
13.7%
757
 
3.4%
9078
38.2%
ValueCountFrequency (%)
9001
 
0.5%
3601
 
0.5%
3001
 
0.5%
2701
 
0.5%
2404
 
2.0%
2111
 
0.5%
18012
5.9%
1651
 
0.5%
15012
5.9%
1301
 
0.5%

LIQUIDITY_INDEX_B
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.5075
Minimum0.62
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:17.306993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.62
5-th percentile1
Q12
median5.5
Q315
95-th percentile90
Maximum1500
Range1499.38
Interquartile range (IQR)13

Descriptive statistics

Standard deviation135.6202159
Coefficient of variation (CV)4.047458505
Kurtosis78.79735243
Mean33.5075
Median Absolute Deviation (MAD)4.325
Skewness8.330667105
Sum6835.53
Variance18392.84295
MonotonicityNot monotonic
2022-06-14T22:46:17.509502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1023
 
11.3%
516
 
7.8%
211
 
5.4%
110
 
4.9%
209
 
4.4%
1.29
 
4.4%
49
 
4.4%
38
 
3.9%
1.57
 
3.4%
125
 
2.5%
Other values (63)97
47.5%
ValueCountFrequency (%)
0.621
 
0.5%
0.791
 
0.5%
0.81
 
0.5%
110
4.9%
1.152
 
1.0%
1.29
4.4%
1.381
 
0.5%
1.421
 
0.5%
1.57
3.4%
1.64
 
2.0%
ValueCountFrequency (%)
15001
 
0.5%
9501
 
0.5%
6001
 
0.5%
4001
 
0.5%
2502
1.0%
1503
1.5%
1001
 
0.5%
902
1.0%
851
 
0.5%
801
 
0.5%

DEBT_INDEX_B
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.475745098
Minimum0.01
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:17.737200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.05
Q10.2
median0.4
Q30.5
95-th percentile0.7
Maximum20
Range19.99
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation1.388234465
Coefficient of variation (CV)2.918021585
Kurtosis195.4689696
Mean0.475745098
Median Absolute Deviation (MAD)0.135
Skewness13.83484204
Sum97.052
Variance1.92719493
MonotonicityNot monotonic
2022-06-14T22:46:17.953818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.530
14.7%
0.326
12.7%
0.421
 
10.3%
0.720
 
9.8%
0.614
 
6.9%
0.213
 
6.4%
0.18
 
3.9%
0.156
 
2.9%
0.055
 
2.5%
0.355
 
2.5%
Other values (37)56
27.5%
ValueCountFrequency (%)
0.013
 
1.5%
0.022
 
1.0%
0.032
 
1.0%
0.042
 
1.0%
0.055
2.5%
0.061
 
0.5%
0.0721
 
0.5%
0.081
 
0.5%
0.18
3.9%
0.122
 
1.0%
ValueCountFrequency (%)
201
 
0.5%
11
 
0.5%
0.720
9.8%
0.661
 
0.5%
0.651
 
0.5%
0.641
 
0.5%
0.631
 
0.5%
0.614
6.9%
0.561
 
0.5%
0.551
 
0.5%

INTEREST_COVERAGE_RATIO_B
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct59
Distinct (%)29.8%
Missing6
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean29.30494949
Minimum0
Maximum1000
Zeros3
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:18.176248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median9.82
Q320
95-th percentile100
Maximum1000
Range1000
Interquartile range (IQR)17

Descriptive statistics

Standard deviation103.0212026
Coefficient of variation (CV)3.515488148
Kurtosis72.49242272
Mean29.30494949
Median Absolute Deviation (MAD)7.59
Skewness8.210809942
Sum5802.38
Variance10613.36819
MonotonicityNot monotonic
2022-06-14T22:46:18.473543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1027
 
13.2%
124
 
11.8%
514
 
6.9%
2010
 
4.9%
210
 
4.9%
159
 
4.4%
47
 
3.4%
37
 
3.4%
66
 
2.9%
306
 
2.9%
Other values (49)78
38.2%
ValueCountFrequency (%)
03
 
1.5%
0.081
 
0.5%
0.51
 
0.5%
0.581
 
0.5%
124
11.8%
1.52
 
1.0%
1.661
 
0.5%
1.81
 
0.5%
210
4.9%
2.233
 
1.5%
ValueCountFrequency (%)
10001
 
0.5%
9501
 
0.5%
4001
 
0.5%
1541
 
0.5%
1501
 
0.5%
1006
2.9%
852
 
1.0%
802
 
1.0%
751
 
0.5%
702
 
1.0%

ROE_B
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct37
Distinct (%)18.4%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.1497164179
Minimum0
Maximum1
Zeros10
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:18.732952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.07
median0.1
Q30.2
95-th percentile0.43
Maximum1
Range1
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.1318926237
Coefficient of variation (CV)0.8809496347
Kurtosis9.585708594
Mean0.1497164179
Median Absolute Deviation (MAD)0.05
Skewness2.41988232
Sum30.093
Variance0.01739566418
MonotonicityNot monotonic
2022-06-14T22:46:18.974046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.132
15.7%
0.219
 
9.3%
0.1516
 
7.8%
0.0513
 
6.4%
010
 
4.9%
0.0310
 
4.9%
0.39
 
4.4%
0.078
 
3.9%
0.098
 
3.9%
0.257
 
3.4%
Other values (27)69
33.8%
ValueCountFrequency (%)
010
4.9%
0.011
 
0.5%
0.023
 
1.5%
0.0310
4.9%
0.046
2.9%
0.0513
6.4%
0.065
 
2.5%
0.078
3.9%
0.086
2.9%
0.098
3.9%
ValueCountFrequency (%)
11
 
0.5%
0.651
 
0.5%
0.551
 
0.5%
0.56
2.9%
0.451
 
0.5%
0.431
 
0.5%
0.41
 
0.5%
0.371
 
0.5%
0.351
 
0.5%
0.321
 
0.5%

ROA_B
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct34
Distinct (%)16.9%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.1226517413
Minimum0
Maximum1
Zeros11
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:19.202770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.1
Q30.15
95-th percentile0.32
Maximum1
Range1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1193452056
Coefficient of variation (CV)0.9730412661
Kurtosis15.62019351
Mean0.1226517413
Median Absolute Deviation (MAD)0.05
Skewness3.055628095
Sum24.653
Variance0.01424327811
MonotonicityNot monotonic
2022-06-14T22:46:19.468738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.130
14.7%
0.0517
 
8.3%
0.1517
 
8.3%
0.214
 
6.9%
011
 
5.4%
0.0711
 
5.4%
0.0110
 
4.9%
0.069
 
4.4%
0.048
 
3.9%
0.138
 
3.9%
Other values (24)66
32.4%
ValueCountFrequency (%)
011
5.4%
0.0110
4.9%
0.026
 
2.9%
0.035
 
2.5%
0.048
3.9%
0.0517
8.3%
0.069
4.4%
0.0711
5.4%
0.087
3.4%
0.095
 
2.5%
ValueCountFrequency (%)
11
 
0.5%
0.55
2.5%
0.451
 
0.5%
0.411
 
0.5%
0.41
 
0.5%
0.331
 
0.5%
0.321
 
0.5%
0.35
2.5%
0.291
 
0.5%
0.252
 
1.0%

WORKING_CAPITAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)29.5%
Missing109
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean1.028513958
Minimum0.1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:19.673022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.7
median1
Q31
95-th percentile2
Maximum3
Range2.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.5931191688
Coefficient of variation (CV)0.5766758576
Kurtosis2.546493483
Mean1.028513958
Median Absolute Deviation (MAD)0.19
Skewness1.464046257
Sum97.70882601
Variance0.3517903484
MonotonicityNot monotonic
2022-06-14T22:46:19.871072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
142
 
20.6%
210
 
4.9%
0.58
 
3.9%
0.83
 
1.5%
0.73
 
1.5%
33
 
1.5%
0.33
 
1.5%
0.42
 
1.0%
0.92
 
1.0%
0.61025458231
 
0.5%
Other values (18)18
 
8.8%
(Missing)109
53.4%
ValueCountFrequency (%)
0.11
 
0.5%
0.151
 
0.5%
0.211
 
0.5%
0.251
 
0.5%
0.33
 
1.5%
0.331
 
0.5%
0.42
 
1.0%
0.42857142861
 
0.5%
0.58
3.9%
0.541
 
0.5%
ValueCountFrequency (%)
33
 
1.5%
2.321
 
0.5%
210
 
4.9%
1.51
 
0.5%
1.491
 
0.5%
1.171
 
0.5%
1.031
 
0.5%
142
20.6%
0.991
 
0.5%
0.92
 
1.0%

NET_EQUITY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)28.1%
Missing147
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean1.084057644
Minimum0.001285714286
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:20.086899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.001285714286
5-th percentile0.28
Q10.5
median1
Q31.49
95-th percentile2.2
Maximum3
Range2.998714286
Interquartile range (IQR)0.99

Descriptive statistics

Standard deviation0.6996247213
Coefficient of variation (CV)0.6453759402
Kurtosis1.08632067
Mean1.084057644
Median Absolute Deviation (MAD)0.5
Skewness1.141515524
Sum61.79128571
Variance0.4894747506
MonotonicityNot monotonic
2022-06-14T22:46:20.277500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
119
 
9.3%
0.58
 
3.9%
28
 
3.9%
0.83
 
1.5%
33
 
1.5%
1.52
 
1.0%
0.92
 
1.0%
0.42
 
1.0%
0.32
 
1.0%
0.22
 
1.0%
Other values (6)6
 
2.9%
(Missing)147
72.1%
ValueCountFrequency (%)
0.0012857142861
 
0.5%
0.22
 
1.0%
0.32
 
1.0%
0.371
 
0.5%
0.42
 
1.0%
0.58
3.9%
0.61
 
0.5%
0.751
 
0.5%
0.83
 
1.5%
0.92
 
1.0%
ValueCountFrequency (%)
33
 
1.5%
28
3.9%
1.581
 
0.5%
1.52
 
1.0%
1.491
 
0.5%
119
9.3%
0.92
 
1.0%
0.83
 
1.5%
0.751
 
0.5%
0.61
 
0.5%

EXPERIENCE_B_VALUE
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct53
Distinct (%)26.9%
Missing7
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean2.42485777
Minimum0.3
Maximum29.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:20.534571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.7
Q11
median1.48
Q32.14
95-th percentile6.048
Maximum29.16
Range28.86
Interquartile range (IQR)1.14

Descriptive statistics

Standard deviation3.485919749
Coefficient of variation (CV)1.437576996
Kurtosis33.97656944
Mean2.42485777
Median Absolute Deviation (MAD)0.52
Skewness5.356279342
Sum477.6969807
Variance12.1516365
MonotonicityNot monotonic
2022-06-14T22:46:20.787680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170
34.3%
240
19.6%
312
 
5.9%
59
 
4.4%
0.55
 
2.5%
45
 
2.5%
0.753
 
1.5%
1.53
 
1.5%
0.92
 
1.0%
102
 
1.0%
Other values (43)46
22.5%
(Missing)7
 
3.4%
ValueCountFrequency (%)
0.31
 
0.5%
0.341
 
0.5%
0.55
2.5%
0.62
 
1.0%
0.72
 
1.0%
0.753
1.5%
0.781
 
0.5%
0.791
 
0.5%
0.81
 
0.5%
0.871
 
0.5%
ValueCountFrequency (%)
29.161
0.5%
25.771
0.5%
23.861
0.5%
11.171
0.5%
11.071
0.5%
102
1.0%
6.881
0.5%
6.451
0.5%
6.241
0.5%
61
0.5%

K_CONTRACTING_B_VALUE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)10.8%
Missing9
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean0.9246153846
Minimum0.25
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:21.064055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile0.5
Q10.7
median1
Q31
95-th percentile1
Maximum5
Range4.75
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.4696063616
Coefficient of variation (CV)0.5078937355
Kurtosis57.5684813
Mean0.9246153846
Median Absolute Deviation (MAD)0
Skewness6.715069921
Sum180.3
Variance0.2205301348
MonotonicityNot monotonic
2022-06-14T22:46:21.236200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1130
63.7%
0.522
 
10.8%
0.610
 
4.9%
0.79
 
4.4%
0.93
 
1.5%
0.542
 
1.0%
0.252
 
1.0%
0.82
 
1.0%
52
 
1.0%
0.652
 
1.0%
Other values (11)11
 
5.4%
(Missing)9
 
4.4%
ValueCountFrequency (%)
0.252
 
1.0%
0.441
 
0.5%
0.522
10.8%
0.511
 
0.5%
0.542
 
1.0%
0.561
 
0.5%
0.610
4.9%
0.641
 
0.5%
0.652
 
1.0%
0.691
 
0.5%
ValueCountFrequency (%)
52
 
1.0%
21
 
0.5%
1.231
 
0.5%
1.031
 
0.5%
1130
63.7%
0.991
 
0.5%
0.951
 
0.5%
0.93
 
1.5%
0.82
 
1.0%
0.781
 
0.5%

PROJECT_INTENSITY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct186
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.36163652
Minimum2.41
Maximum178.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:21.433648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.41
5-th percentile3.403
Q15.138155138
median7.27
Q312.095
95-th percentile44.626
Maximum178.05
Range175.64
Interquartile range (IQR)6.956844862

Descriptive statistics

Standard deviation20.26916926
Coefficient of variation (CV)1.516967568
Kurtosis30.51497284
Mean13.36163652
Median Absolute Deviation (MAD)2.838161342
Skewness4.969870579
Sum2725.773851
Variance410.8392227
MonotonicityNot monotonic
2022-06-14T22:46:21.665600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.453
 
1.5%
4.523
 
1.5%
5.512
 
1.0%
4.272
 
1.0%
3.72
 
1.0%
5.352
 
1.0%
5.782
 
1.0%
11.642
 
1.0%
7.532
 
1.0%
4.692
 
1.0%
Other values (176)182
89.2%
ValueCountFrequency (%)
2.411
0.5%
2.491
0.5%
3.091
0.5%
3.111
0.5%
3.1307118721
0.5%
3.171
0.5%
3.191
0.5%
3.2804724021
0.5%
3.291
0.5%
3.3543341491
0.5%
ValueCountFrequency (%)
178.051
0.5%
132.431
0.5%
114.471
0.5%
971
0.5%
69.841
0.5%
57.551
0.5%
57.161
0.5%
52.271
0.5%
48.521
0.5%
45.181
0.5%

PRICE_SCORE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4657843137
Minimum0
Maximum1
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:21.934996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.3
median0.5
Q30.6
95-th percentile0.8
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.1831386794
Coefficient of variation (CV)0.3931834415
Kurtosis-0.2139442444
Mean0.4657843137
Median Absolute Deviation (MAD)0.1
Skewness-0.01455611741
Sum95.02
Variance0.03353977591
MonotonicityNot monotonic
2022-06-14T22:46:22.191473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.540
19.6%
0.640
19.6%
0.426
12.7%
0.317
8.3%
0.215
 
7.4%
0.813
 
6.4%
0.79
 
4.4%
0.277
 
3.4%
0.256
 
2.9%
0.554
 
2.0%
Other values (17)27
13.2%
ValueCountFrequency (%)
02
 
1.0%
0.14
 
2.0%
0.121
 
0.5%
0.151
 
0.5%
0.215
7.4%
0.241
 
0.5%
0.256
 
2.9%
0.277
3.4%
0.291
 
0.5%
0.317
8.3%
ValueCountFrequency (%)
11
 
0.5%
0.91
 
0.5%
0.813
 
6.4%
0.79
 
4.4%
0.652
 
1.0%
0.640
19.6%
0.592
 
1.0%
0.554
 
2.0%
0.540
19.6%
0.481
 
0.5%

TECHNICAL_SCORE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct29
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3944705882
Minimum0
Maximum0.9
Zeros9
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:22.409744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.3
median0.4
Q30.5
95-th percentile0.7
Maximum0.9
Range0.9
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1904651659
Coefficient of variation (CV)0.4828374322
Kurtosis-0.2681770377
Mean0.3944705882
Median Absolute Deviation (MAD)0.1
Skewness0.03332266944
Sum80.472
Variance0.03627697943
MonotonicityNot monotonic
2022-06-14T22:46:22.617694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.341
20.1%
0.435
17.2%
0.528
13.7%
0.616
 
7.8%
0.114
 
6.9%
0.211
 
5.4%
0.710
 
4.9%
09
 
4.4%
0.455
 
2.5%
0.84
 
2.0%
Other values (19)31
15.2%
ValueCountFrequency (%)
09
 
4.4%
0.114
 
6.9%
0.192
 
1.0%
0.1921
 
0.5%
0.211
 
5.4%
0.253
 
1.5%
0.291
 
0.5%
0.341
20.1%
0.31
 
0.5%
0.333
 
1.5%
ValueCountFrequency (%)
0.91
 
0.5%
0.84
 
2.0%
0.762
 
1.0%
0.751
 
0.5%
0.710
4.9%
0.691
 
0.5%
0.652
 
1.0%
0.641
 
0.5%
0.611
 
0.5%
0.616
7.8%

NATIONAL_INDUSTRY_SCORE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09196078431
Minimum0
Maximum0.3
Zeros22
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:22.794506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.1
Q30.1
95-th percentile0.15
Maximum0.3
Range0.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04239247112
Coefficient of variation (CV)0.4609842275
Kurtosis3.884897213
Mean0.09196078431
Median Absolute Deviation (MAD)0
Skewness0.03359629553
Sum18.76
Variance0.001797121607
MonotonicityNot monotonic
2022-06-14T22:46:22.968247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.1145
71.1%
022
 
10.8%
0.159
 
4.4%
0.059
 
4.4%
0.26
 
2.9%
0.074
 
2.0%
0.062
 
1.0%
0.092
 
1.0%
0.041
 
0.5%
0.081
 
0.5%
Other values (3)3
 
1.5%
ValueCountFrequency (%)
022
 
10.8%
0.041
 
0.5%
0.059
 
4.4%
0.062
 
1.0%
0.074
 
2.0%
0.081
 
0.5%
0.092
 
1.0%
0.1145
71.1%
0.121
 
0.5%
0.141
 
0.5%
ValueCountFrequency (%)
0.31
 
0.5%
0.26
 
2.9%
0.159
 
4.4%
0.141
 
0.5%
0.121
 
0.5%
0.1145
71.1%
0.092
 
1.0%
0.081
 
0.5%
0.074
 
2.0%
0.062
 
1.0%

OTHER_SCORE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04783333333
Minimum0
Maximum0.5
Zeros143
Zeros (%)70.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:23.137871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.05
95-th percentile0.3
Maximum0.5
Range0.5
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.09815555999
Coefficient of variation (CV)2.052032613
Kurtosis5.061809207
Mean0.04783333333
Median Absolute Deviation (MAD)0
Skewness2.32363616
Sum9.758
Variance0.009634513957
MonotonicityNot monotonic
2022-06-14T22:46:23.326139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0143
70.1%
0.116
 
7.8%
0.213
 
6.4%
0.018
 
3.9%
0.44
 
2.0%
0.34
 
2.0%
0.053
 
1.5%
0.062
 
1.0%
0.152
 
1.0%
0.161
 
0.5%
Other values (8)8
 
3.9%
ValueCountFrequency (%)
0143
70.1%
0.0081
 
0.5%
0.018
 
3.9%
0.053
 
1.5%
0.062
 
1.0%
0.081
 
0.5%
0.116
 
7.8%
0.141
 
0.5%
0.152
 
1.0%
0.161
 
0.5%
ValueCountFrequency (%)
0.51
 
0.5%
0.44
 
2.0%
0.351
 
0.5%
0.331
 
0.5%
0.311
 
0.5%
0.34
 
2.0%
0.231
 
0.5%
0.213
6.4%
0.161
 
0.5%
0.152
 
1.0%

NUMBER_BIDDERS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.024509804
Minimum1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:23.510441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum65
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.175195411
Coefficient of variation (CV)2.556270857
Kurtosis112.9577565
Mean2.024509804
Median Absolute Deviation (MAD)0
Skewness9.92938939
Sum413
Variance26.78264754
MonotonicityNot monotonic
2022-06-14T22:46:23.677240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1158
77.5%
227
 
13.2%
54
 
2.0%
44
 
2.0%
113
 
1.5%
33
 
1.5%
62
 
1.0%
651
 
0.5%
301
 
0.5%
161
 
0.5%
ValueCountFrequency (%)
1158
77.5%
227
 
13.2%
33
 
1.5%
44
 
2.0%
54
 
2.0%
62
 
1.0%
113
 
1.5%
161
 
0.5%
301
 
0.5%
651
 
0.5%
ValueCountFrequency (%)
651
 
0.5%
301
 
0.5%
161
 
0.5%
113
 
1.5%
62
 
1.0%
54
 
2.0%
44
 
2.0%
33
 
1.5%
227
 
13.2%
1158
77.5%

CONTRACT_VALUE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct203
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1203.388787
Minimum237.45
Maximum13430.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:23.878929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum237.45
5-th percentile309.353
Q1423.1725
median649.195
Q31203.943178
95-th percentile4294.475
Maximum13430.77
Range13193.32
Interquartile range (IQR)780.7706784

Descriptive statistics

Standard deviation1596.026669
Coefficient of variation (CV)1.326276833
Kurtosis23.15160307
Mean1203.388787
Median Absolute Deviation (MAD)269.835
Skewness4.169360078
Sum245491.3125
Variance2547301.127
MonotonicityNot monotonic
2022-06-14T22:46:24.157163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
519.92
 
1.0%
861.071
 
0.5%
393.271
 
0.5%
1392.441
 
0.5%
830.471
 
0.5%
376.011
 
0.5%
310.331
 
0.5%
423.591
 
0.5%
698.271
 
0.5%
482.611
 
0.5%
Other values (193)193
94.6%
ValueCountFrequency (%)
237.451
0.5%
277.641
0.5%
2801
0.5%
281.651
0.5%
287.41
0.5%
2881
0.5%
288.661
0.5%
291.461
0.5%
295.811
0.5%
305.721
0.5%
ValueCountFrequency (%)
13430.771
0.5%
10302.161
0.5%
8131.171
0.5%
5819.031
0.5%
5377.1788641
0.5%
5144.461
0.5%
5136.191
0.5%
4704.561
0.5%
4573.021
0.5%
4379.251
0.5%

AWARD_GROWTH
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct134
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2162089003
Minimum-6.5456
Maximum0.0001
Zeros49
Zeros (%)24.0%
Negative154
Negative (%)75.5%
Memory size1.7 KiB
2022-06-14T22:46:24.499458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-6.5456
5-th percentile-1.13973
Q1-0.07002140153
median-0.0096
Q3-0.0001
95-th percentile0
Maximum0.0001
Range6.5457
Interquartile range (IQR)0.06992140153

Descriptive statistics

Standard deviation0.7230896794
Coefficient of variation (CV)-3.344402928
Kurtosis40.16723524
Mean-0.2162089003
Median Absolute Deviation (MAD)0.0096
Skewness-5.7696287
Sum-44.10661567
Variance0.5228586844
MonotonicityNot monotonic
2022-06-14T22:46:24.727468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
049
 
24.0%
-0.00034
 
2.0%
-0.00014
 
2.0%
-0.00024
 
2.0%
-0.00513
 
1.5%
-0.0262
 
1.0%
-0.00962
 
1.0%
-0.01062
 
1.0%
-0.02742
 
1.0%
-0.02572
 
1.0%
Other values (124)130
63.7%
ValueCountFrequency (%)
-6.54561
0.5%
-5.11311
0.5%
-2.8420031041
0.5%
-2.76531
0.5%
-2.2213716471
0.5%
-2.12811
0.5%
-2.01771
0.5%
-1.90071
0.5%
-1.79031
0.5%
-1.32891
0.5%
ValueCountFrequency (%)
0.00011
 
0.5%
049
24.0%
-0.00014
 
2.0%
-0.00024
 
2.0%
-0.00034
 
2.0%
-0.00042
 
1.0%
-0.00052
 
1.0%
-0.00062853947361
 
0.5%
-0.00072
 
1.0%
-0.00081
 
0.5%

ADDITIONAL_COST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct70
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.62905632
Minimum0
Maximum2249.93
Zeros134
Zeros (%)65.7%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:25.019040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q347.2825
95-th percentile361.9736359
Maximum2249.93
Range2249.93
Interquartile range (IQR)47.2825

Descriptive statistics

Standard deviation236.9827735
Coefficient of variation (CV)2.800253055
Kurtosis38.32319388
Mean84.62905632
Median Absolute Deviation (MAD)0
Skewness5.374351597
Sum17264.32749
Variance56160.83493
MonotonicityNot monotonic
2022-06-14T22:46:25.273423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0134
65.7%
124.162
 
1.0%
153.741
 
0.5%
57.981
 
0.5%
550.161
 
0.5%
461.851
 
0.5%
279.51
 
0.5%
28.181
 
0.5%
201.981
 
0.5%
41.131
 
0.5%
Other values (60)60
29.4%
ValueCountFrequency (%)
0134
65.7%
10.281
 
0.5%
12.161
 
0.5%
13.211
 
0.5%
15.681
 
0.5%
20.331
 
0.5%
24.351
 
0.5%
25.206097941
 
0.5%
25.251
 
0.5%
25.721
 
0.5%
ValueCountFrequency (%)
2249.931
0.5%
1174.5697511
0.5%
1025.271
0.5%
931.171
0.5%
840.651
0.5%
828.981
0.5%
800.31
0.5%
550.161
0.5%
461.851
0.5%
443.921
0.5%

FINAL_COST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct203
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1288.017892
Minimum277.64
Maximum13430.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:26.000101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum277.64
5-th percentile317.1625
Q1463.9
median685.865
Q31279.8825
95-th percentile4373.5155
Maximum13430.77
Range13153.13
Interquartile range (IQR)815.9825

Descriptive statistics

Standard deviation1669.854833
Coefficient of variation (CV)1.296453134
Kurtosis19.30650787
Mean1288.017892
Median Absolute Deviation (MAD)302.02
Skewness3.846118352
Sum262755.65
Variance2788415.164
MonotonicityNot monotonic
2022-06-14T22:46:26.274537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
519.92
 
1.0%
1014.811
 
0.5%
393.271
 
0.5%
1450.421
 
0.5%
830.471
 
0.5%
376.011
 
0.5%
351.461
 
0.5%
625.571
 
0.5%
698.271
 
0.5%
482.611
 
0.5%
Other values (193)193
94.6%
ValueCountFrequency (%)
277.641
0.5%
2801
0.5%
281.651
0.5%
287.41
0.5%
2881
0.5%
288.661
0.5%
295.811
0.5%
305.721
0.5%
309.21
0.5%
316.151
0.5%
ValueCountFrequency (%)
13430.771
0.5%
10302.161
0.5%
8131.171
0.5%
7386.121
0.5%
6369.191
0.5%
6075.631
0.5%
5377.1788641
0.5%
4871.371
0.5%
4704.561
0.5%
4573.021
0.5%

ADDITIONAL_TIME
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct40
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.40686275
Minimum0
Maximum295
Zeros119
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:26.572941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q339.25
95-th percentile120
Maximum295
Range295
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation46.77328352
Coefficient of variation (CV)1.771254843
Kurtosis10.33251921
Mean26.40686275
Median Absolute Deviation (MAD)0
Skewness2.821199179
Sum5387
Variance2187.740051
MonotonicityNot monotonic
2022-06-14T22:46:26.830683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0119
58.3%
3018
 
8.8%
458
 
3.9%
608
 
3.9%
755
 
2.5%
904
 
2.0%
153
 
1.5%
403
 
1.5%
1203
 
1.5%
202
 
1.0%
Other values (30)31
 
15.2%
ValueCountFrequency (%)
0119
58.3%
61
 
0.5%
71
 
0.5%
101
 
0.5%
111
 
0.5%
141
 
0.5%
153
 
1.5%
202
 
1.0%
221
 
0.5%
231
 
0.5%
ValueCountFrequency (%)
2951
 
0.5%
2801
 
0.5%
2111
 
0.5%
1901
 
0.5%
1502
1.0%
1351
 
0.5%
1321
 
0.5%
1291
 
0.5%
1211
 
0.5%
1203
1.5%

FINAL_DEADLINE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.2647059
Minimum10
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:27.235837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile57.45
Q190
median120
Q3150
95-th percentile267.75
Maximum900
Range890
Interquartile range (IQR)60

Descriptive statistics

Standard deviation86.04918132
Coefficient of variation (CV)0.650583092
Kurtosis31.26267603
Mean132.2647059
Median Absolute Deviation (MAD)30
Skewness4.18698421
Sum26982
Variance7404.461605
MonotonicityNot monotonic
2022-06-14T22:46:27.471498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9048
23.5%
12031
15.2%
15018
 
8.8%
6016
 
7.8%
18011
 
5.4%
1357
 
3.4%
1655
 
2.5%
755
 
2.5%
2404
 
2.0%
304
 
2.0%
Other values (46)55
27.0%
ValueCountFrequency (%)
101
 
0.5%
304
 
2.0%
352
 
1.0%
452
 
1.0%
531
 
0.5%
571
 
0.5%
6016
7.8%
691
 
0.5%
701
 
0.5%
755
 
2.5%
ValueCountFrequency (%)
9001
0.5%
3901
0.5%
3851
0.5%
3701
0.5%
3601
0.5%
3411
0.5%
3301
0.5%
3001
0.5%
2862
1.0%
2701
0.5%

TIME_SUSPENDED_P
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct63
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3502941176
Minimum0
Maximum5.39
Zeros129
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:27.825313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3475
95-th percentile1.534
Maximum5.39
Range5.39
Interquartile range (IQR)0.3475

Descriptive statistics

Standard deviation0.8221457205
Coefficient of variation (CV)2.347015491
Kurtosis17.84707064
Mean0.3502941176
Median Absolute Deviation (MAD)0
Skewness3.968403383
Sum71.46
Variance0.6759235858
MonotonicityNot monotonic
2022-06-14T22:46:28.066067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0129
63.2%
0.253
 
1.5%
0.692
 
1.0%
0.292
 
1.0%
0.772
 
1.0%
0.452
 
1.0%
0.12
 
1.0%
0.272
 
1.0%
0.632
 
1.0%
0.232
 
1.0%
Other values (53)56
27.5%
ValueCountFrequency (%)
0129
63.2%
0.12
 
1.0%
0.141
 
0.5%
0.151
 
0.5%
0.161
 
0.5%
0.181
 
0.5%
0.211
 
0.5%
0.232
 
1.0%
0.253
 
1.5%
0.272
 
1.0%
ValueCountFrequency (%)
5.391
0.5%
5.11
0.5%
4.291
0.5%
4.171
0.5%
3.971
0.5%
2.881
0.5%
2.781
0.5%
1.931
0.5%
1.891
0.5%
1.871
0.5%

IDI
Real number (ℝ≥0)

HIGH CORRELATION

Distinct158
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.50764706
Minimum34.9
Maximum94.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-06-14T22:46:28.300906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum34.9
5-th percentile45.845
Q159.03
median74.51
Q381.795
95-th percentile88.19
Maximum94.5
Range59.6
Interquartile range (IQR)22.765

Descriptive statistics

Standard deviation13.95389115
Coefficient of variation (CV)0.1979060674
Kurtosis-0.7509737908
Mean70.50764706
Median Absolute Deviation (MAD)8.73
Skewness-0.5727021834
Sum14383.56
Variance194.7110782
MonotonicityNot monotonic
2022-06-14T22:46:28.539427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.194
 
2.0%
82.774
 
2.0%
50.13
 
1.5%
76.893
 
1.5%
62.923
 
1.5%
86.593
 
1.5%
84.873
 
1.5%
82.893
 
1.5%
51.13
 
1.5%
77.392
 
1.0%
Other values (148)173
84.8%
ValueCountFrequency (%)
34.91
0.5%
39.11
0.5%
39.41
0.5%
40.181
0.5%
40.71
0.5%
41.31
0.5%
441
0.5%
44.92
1.0%
45.51
0.5%
45.81
0.5%
ValueCountFrequency (%)
94.52
1.0%
90.621
 
0.5%
90.41
 
0.5%
89.772
1.0%
89.581
 
0.5%
88.341
 
0.5%
88.212
1.0%
88.194
2.0%
87.271
 
0.5%
86.821
 
0.5%

CONTRACTOR
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
CONSORTIUM
83 
COMPANY
66 
INDIVIDUAL
55 

Length

Max length10
Median length10
Mean length9.029411765
Min length7

Characters and Unicode

Total characters1842
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowCOMPANY
4th rowCONSORTIUM
5th rowCONSORTIUM

Common Values

ValueCountFrequency (%)
CONSORTIUM83
40.7%
COMPANY66
32.4%
INDIVIDUAL55
27.0%

Length

2022-06-14T22:46:28.794291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:28.956308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
consortium83
40.7%
company66
32.4%
individual55
27.0%

Most occurring characters

ValueCountFrequency (%)
I248
13.5%
O232
12.6%
N204
11.1%
C149
8.1%
M149
8.1%
U138
7.5%
A121
 
6.6%
D110
 
6.0%
S83
 
4.5%
R83
 
4.5%
Other values (5)325
17.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1842
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I248
13.5%
O232
12.6%
N204
11.1%
C149
8.1%
M149
8.1%
U138
7.5%
A121
 
6.6%
D110
 
6.0%
S83
 
4.5%
R83
 
4.5%
Other values (5)325
17.6%

Most occurring scripts

ValueCountFrequency (%)
Latin1842
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I248
13.5%
O232
12.6%
N204
11.1%
C149
8.1%
M149
8.1%
U138
7.5%
A121
 
6.6%
D110
 
6.0%
S83
 
4.5%
R83
 
4.5%
Other values (5)325
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I248
13.5%
O232
12.6%
N204
11.1%
C149
8.1%
M149
8.1%
U138
7.5%
A121
 
6.6%
D110
 
6.0%
S83
 
4.5%
R83
 
4.5%
Other values (5)325
17.6%

HIGHEST SCORE
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
PRICE
113 
TECHNICAL
75 
OTHER
16 

Length

Max length9
Median length5
Mean length6.470588235
Min length5

Characters and Unicode

Total characters1320
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRICE
2nd rowTECHNICAL
3rd rowPRICE
4th rowPRICE
5th rowTECHNICAL

Common Values

ValueCountFrequency (%)
PRICE113
55.4%
TECHNICAL75
36.8%
OTHER16
 
7.8%

Length

2022-06-14T22:46:29.119249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:29.279730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
price113
55.4%
technical75
36.8%
other16
 
7.8%

Most occurring characters

ValueCountFrequency (%)
C263
19.9%
E204
15.5%
I188
14.2%
R129
9.8%
P113
8.6%
T91
 
6.9%
H91
 
6.9%
N75
 
5.7%
A75
 
5.7%
L75
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1320
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C263
19.9%
E204
15.5%
I188
14.2%
R129
9.8%
P113
8.6%
T91
 
6.9%
H91
 
6.9%
N75
 
5.7%
A75
 
5.7%
L75
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1320
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C263
19.9%
E204
15.5%
I188
14.2%
R129
9.8%
P113
8.6%
T91
 
6.9%
H91
 
6.9%
N75
 
5.7%
A75
 
5.7%
L75
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C263
19.9%
E204
15.5%
I188
14.2%
R129
9.8%
P113
8.6%
T91
 
6.9%
H91
 
6.9%
N75
 
5.7%
A75
 
5.7%
L75
 
5.7%

LOWEST_SCORE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
OTHER
169 
NATIONAL_INDUSTRY
35 

Length

Max length17
Median length5
Mean length7.058823529
Min length5

Characters and Unicode

Total characters1440
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNATIONAL_INDUSTRY
2nd rowOTHER
3rd rowOTHER
4th rowOTHER
5th rowNATIONAL_INDUSTRY

Common Values

ValueCountFrequency (%)
OTHER169
82.8%
NATIONAL_INDUSTRY35
 
17.2%

Length

2022-06-14T22:46:29.416370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:29.536759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
other169
82.8%
national_industry35
 
17.2%

Most occurring characters

ValueCountFrequency (%)
T239
16.6%
O204
14.2%
R204
14.2%
H169
11.7%
E169
11.7%
N105
7.3%
A70
 
4.9%
I70
 
4.9%
L35
 
2.4%
_35
 
2.4%
Other values (4)140
9.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1405
97.6%
Connector Punctuation35
 
2.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T239
17.0%
O204
14.5%
R204
14.5%
H169
12.0%
E169
12.0%
N105
7.5%
A70
 
5.0%
I70
 
5.0%
L35
 
2.5%
D35
 
2.5%
Other values (3)105
7.5%
Connector Punctuation
ValueCountFrequency (%)
_35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1405
97.6%
Common35
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T239
17.0%
O204
14.5%
R204
14.5%
H169
12.0%
E169
12.0%
N105
7.5%
A70
 
5.0%
I70
 
5.0%
L35
 
2.5%
D35
 
2.5%
Other values (3)105
7.5%
Common
ValueCountFrequency (%)
_35
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T239
16.6%
O204
14.2%
R204
14.2%
H169
11.7%
E169
11.7%
N105
7.3%
A70
 
4.9%
I70
 
4.9%
L35
 
2.4%
_35
 
2.4%
Other values (4)140
9.7%

YEAR
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Y_2015
70 
Y_2017
50 
Y_2018
38 
Y_2016
30 
Y_2019
16 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1224
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY_2016
2nd rowY_2017
3rd rowY_2017
4th rowY_2018
5th rowY_2015

Common Values

ValueCountFrequency (%)
Y_201570
34.3%
Y_201750
24.5%
Y_201838
18.6%
Y_201630
14.7%
Y_201916
 
7.8%

Length

2022-06-14T22:46:29.660513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:29.788217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
y_201570
34.3%
y_201750
24.5%
y_201838
18.6%
y_201630
14.7%
y_201916
 
7.8%

Most occurring characters

ValueCountFrequency (%)
Y204
16.7%
_204
16.7%
2204
16.7%
0204
16.7%
1204
16.7%
570
 
5.7%
750
 
4.1%
838
 
3.1%
630
 
2.5%
916
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number816
66.7%
Uppercase Letter204
 
16.7%
Connector Punctuation204
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2204
25.0%
0204
25.0%
1204
25.0%
570
 
8.6%
750
 
6.1%
838
 
4.7%
630
 
3.7%
916
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
Y204
100.0%
Connector Punctuation
ValueCountFrequency (%)
_204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1020
83.3%
Latin204
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
_204
20.0%
2204
20.0%
0204
20.0%
1204
20.0%
570
 
6.9%
750
 
4.9%
838
 
3.7%
630
 
2.9%
916
 
1.6%
Latin
ValueCountFrequency (%)
Y204
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y204
16.7%
_204
16.7%
2204
16.7%
0204
16.7%
1204
16.7%
570
 
5.7%
750
 
4.1%
838
 
3.1%
630
 
2.5%
916
 
1.3%

IDI_CAT
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
OUTSTANDING
65 
SATISFACTORY
59 
LOW
51 
MEDIUM
29 

Length

Max length12
Median length11
Mean length8.578431373
Min length3

Characters and Unicode

Total characters1750
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSATISFACTORY
2nd rowSATISFACTORY
3rd rowOUTSTANDING
4th rowLOW
5th rowSATISFACTORY

Common Values

ValueCountFrequency (%)
OUTSTANDING65
31.9%
SATISFACTORY59
28.9%
LOW51
25.0%
MEDIUM29
14.2%

Length

2022-06-14T22:46:29.929712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:30.054666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
outstanding65
31.9%
satisfactory59
28.9%
low51
25.0%
medium29
14.2%

Most occurring characters

ValueCountFrequency (%)
T248
14.2%
S183
10.5%
A183
10.5%
O175
10.0%
I153
8.7%
N130
 
7.4%
U94
 
5.4%
D94
 
5.4%
G65
 
3.7%
F59
 
3.4%
Other values (7)366
20.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1750
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T248
14.2%
S183
10.5%
A183
10.5%
O175
10.0%
I153
8.7%
N130
 
7.4%
U94
 
5.4%
D94
 
5.4%
G65
 
3.7%
F59
 
3.4%
Other values (7)366
20.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1750
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T248
14.2%
S183
10.5%
A183
10.5%
O175
10.0%
I153
8.7%
N130
 
7.4%
U94
 
5.4%
D94
 
5.4%
G65
 
3.7%
F59
 
3.4%
Other values (7)366
20.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T248
14.2%
S183
10.5%
A183
10.5%
O175
10.0%
I153
8.7%
N130
 
7.4%
U94
 
5.4%
D94
 
5.4%
G65
 
3.7%
F59
 
3.4%
Other values (7)366
20.9%

CONTRACT_TYPE
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
MAINTEINANCE
130 
CONSTRUCTION
74 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2448
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONSTRUCTION
2nd rowMAINTEINANCE
3rd rowMAINTEINANCE
4th rowMAINTEINANCE
5th rowMAINTEINANCE

Common Values

ValueCountFrequency (%)
MAINTEINANCE130
63.7%
CONSTRUCTION74
36.3%

Length

2022-06-14T22:46:30.193823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:30.308098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
mainteinance130
63.7%
construction74
36.3%

Most occurring characters

ValueCountFrequency (%)
N538
22.0%
I334
13.6%
T278
11.4%
C278
11.4%
A260
10.6%
E260
10.6%
O148
 
6.0%
M130
 
5.3%
S74
 
3.0%
R74
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2448
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N538
22.0%
I334
13.6%
T278
11.4%
C278
11.4%
A260
10.6%
E260
10.6%
O148
 
6.0%
M130
 
5.3%
S74
 
3.0%
R74
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N538
22.0%
I334
13.6%
T278
11.4%
C278
11.4%
A260
10.6%
E260
10.6%
O148
 
6.0%
M130
 
5.3%
S74
 
3.0%
R74
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N538
22.0%
I334
13.6%
T278
11.4%
C278
11.4%
A260
10.6%
E260
10.6%
O148
 
6.0%
M130
 
5.3%
S74
 
3.0%
R74
 
3.0%

MUNICIPALITY_TYPE
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
TYPE_6
156 
OTHER
29 
TYPE_5
19 

Length

Max length6
Median length6
Mean length5.857843137
Min length5

Characters and Unicode

Total characters1195
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTYPE_6
2nd rowTYPE_6
3rd rowTYPE_6
4th rowTYPE_6
5th rowTYPE_6

Common Values

ValueCountFrequency (%)
TYPE_6156
76.5%
OTHER29
 
14.2%
TYPE_519
 
9.3%

Length

2022-06-14T22:46:30.426949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:30.546000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
type_6156
76.5%
other29
 
14.2%
type_519
 
9.3%

Most occurring characters

ValueCountFrequency (%)
T204
17.1%
E204
17.1%
Y175
14.6%
P175
14.6%
_175
14.6%
6156
13.1%
O29
 
2.4%
H29
 
2.4%
R29
 
2.4%
519
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter845
70.7%
Connector Punctuation175
 
14.6%
Decimal Number175
 
14.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T204
24.1%
E204
24.1%
Y175
20.7%
P175
20.7%
O29
 
3.4%
H29
 
3.4%
R29
 
3.4%
Decimal Number
ValueCountFrequency (%)
6156
89.1%
519
 
10.9%
Connector Punctuation
ValueCountFrequency (%)
_175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin845
70.7%
Common350
29.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T204
24.1%
E204
24.1%
Y175
20.7%
P175
20.7%
O29
 
3.4%
H29
 
3.4%
R29
 
3.4%
Common
ValueCountFrequency (%)
_175
50.0%
6156
44.6%
519
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T204
17.1%
E204
17.1%
Y175
14.6%
P175
14.6%
_175
14.6%
6156
13.1%
O29
 
2.4%
H29
 
2.4%
R29
 
2.4%
519
 
1.6%

DEPARTMENT
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
CUNDINAMARCA
39 
BOYACA
39 
ANTIOQUIA
31 
SANTANDER
25 
CORDOBA
13 
Other values (20)
57 

Length

Max length15
Median length9
Mean length8.509803922
Min length4

Characters and Unicode

Total characters1736
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.5%

Sample

1st rowMAGDALENA
2nd rowBOYACA
3rd rowANTIOQUIA
4th rowTOLIMA
5th rowBOYACA

Common Values

ValueCountFrequency (%)
CUNDINAMARCA39
19.1%
BOYACA39
19.1%
ANTIOQUIA31
15.2%
SANTANDER25
12.3%
CORDOBA13
 
6.4%
NORTE_SANTANDER7
 
3.4%
TOLIMA7
 
3.4%
CASANARE7
 
3.4%
CAUCA4
 
2.0%
ARAUCA4
 
2.0%
Other values (15)28
13.7%

Length

2022-06-14T22:46:30.685773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cundinamarca39
19.1%
boyaca39
19.1%
antioquia31
15.2%
santander25
12.3%
cordoba13
 
6.4%
norte_santander7
 
3.4%
tolima7
 
3.4%
casanare7
 
3.4%
cauca4
 
2.0%
arauca4
 
2.0%
Other values (15)28
13.7%

Most occurring characters

ValueCountFrequency (%)
A425
24.5%
N194
11.2%
C165
 
9.5%
I119
 
6.9%
O117
 
6.7%
R114
 
6.6%
U96
 
5.5%
D87
 
5.0%
T83
 
4.8%
E63
 
3.6%
Other values (12)273
15.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1726
99.4%
Connector Punctuation10
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A425
24.6%
N194
11.2%
C165
 
9.6%
I119
 
6.9%
O117
 
6.8%
R114
 
6.6%
U96
 
5.6%
D87
 
5.0%
T83
 
4.8%
E63
 
3.7%
Other values (11)263
15.2%
Connector Punctuation
ValueCountFrequency (%)
_10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1726
99.4%
Common10
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A425
24.6%
N194
11.2%
C165
 
9.6%
I119
 
6.9%
O117
 
6.8%
R114
 
6.6%
U96
 
5.6%
D87
 
5.0%
T83
 
4.8%
E63
 
3.7%
Other values (11)263
15.2%
Common
ValueCountFrequency (%)
_10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A425
24.5%
N194
11.2%
C165
 
9.5%
I119
 
6.9%
O117
 
6.7%
R114
 
6.6%
U96
 
5.5%
D87
 
5.0%
T83
 
4.8%
E63
 
3.6%
Other values (12)273
15.7%

REGION
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ANDINA
151 
CARIBE
22 
ORINOQUIA
 
13
PACIFICA
 
10
AMAZONIA
 
8

Length

Max length9
Median length6
Mean length6.367647059
Min length6

Characters and Unicode

Total characters1299
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCARIBE
2nd rowANDINA
3rd rowANDINA
4th rowANDINA
5th rowANDINA

Common Values

ValueCountFrequency (%)
ANDINA151
74.0%
CARIBE22
 
10.8%
ORINOQUIA13
 
6.4%
PACIFICA10
 
4.9%
AMAZONIA8
 
3.9%

Length

2022-06-14T22:46:30.874744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:31.001570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
andina151
74.0%
caribe22
 
10.8%
orinoquia13
 
6.4%
pacifica10
 
4.9%
amazonia8
 
3.9%

Most occurring characters

ValueCountFrequency (%)
A381
29.3%
N323
24.9%
I227
17.5%
D151
 
11.6%
C42
 
3.2%
R35
 
2.7%
O34
 
2.6%
B22
 
1.7%
E22
 
1.7%
Q13
 
1.0%
Other values (5)49
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1299
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A381
29.3%
N323
24.9%
I227
17.5%
D151
 
11.6%
C42
 
3.2%
R35
 
2.7%
O34
 
2.6%
B22
 
1.7%
E22
 
1.7%
Q13
 
1.0%
Other values (5)49
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin1299
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A381
29.3%
N323
24.9%
I227
17.5%
D151
 
11.6%
C42
 
3.2%
R35
 
2.7%
O34
 
2.6%
B22
 
1.7%
E22
 
1.7%
Q13
 
1.0%
Other values (5)49
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A381
29.3%
N323
24.9%
I227
17.5%
D151
 
11.6%
C42
 
3.2%
R35
 
2.7%
O34
 
2.6%
B22
 
1.7%
E22
 
1.7%
Q13
 
1.0%
Other values (5)49
 
3.8%

REGION_2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
CENTRO_ORIENTE
110 
EJE_CAFETERO
32 
CARIBE
22 
LLANO
17 
CENTRO_SUR
13 

Length

Max length14
Median length14
Mean length11.5245098
Min length5

Characters and Unicode

Total characters2351
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCARIBE
2nd rowCENTRO_ORIENTE
3rd rowEJE_CAFETERO
4th rowCENTRO_SUR
5th rowCENTRO_ORIENTE

Common Values

ValueCountFrequency (%)
CENTRO_ORIENTE110
53.9%
EJE_CAFETERO32
 
15.7%
CARIBE22
 
10.8%
LLANO17
 
8.3%
CENTRO_SUR13
 
6.4%
PACIFICO10
 
4.9%

Length

2022-06-14T22:46:31.146168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:31.266086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
centro_oriente110
53.9%
eje_cafetero32
 
15.7%
caribe22
 
10.8%
llano17
 
8.3%
centro_sur13
 
6.4%
pacifico10
 
4.9%

Most occurring characters

ValueCountFrequency (%)
E493
21.0%
R300
12.8%
O292
12.4%
T265
11.3%
N250
10.6%
C197
 
8.4%
_155
 
6.6%
I152
 
6.5%
A81
 
3.4%
F42
 
1.8%
Other values (6)124
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2196
93.4%
Connector Punctuation155
 
6.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E493
22.4%
R300
13.7%
O292
13.3%
T265
12.1%
N250
11.4%
C197
 
9.0%
I152
 
6.9%
A81
 
3.7%
F42
 
1.9%
L34
 
1.5%
Other values (5)90
 
4.1%
Connector Punctuation
ValueCountFrequency (%)
_155
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2196
93.4%
Common155
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E493
22.4%
R300
13.7%
O292
13.3%
T265
12.1%
N250
11.4%
C197
 
9.0%
I152
 
6.9%
A81
 
3.7%
F42
 
1.9%
L34
 
1.5%
Other values (5)90
 
4.1%
Common
ValueCountFrequency (%)
_155
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E493
21.0%
R300
12.8%
O292
12.4%
T265
11.3%
N250
10.6%
C197
 
8.4%
_155
 
6.6%
I152
 
6.5%
A81
 
3.4%
F42
 
1.8%
Other values (6)124
 
5.3%

OWNER
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
MUNICIPALITY
195 
DEPARTMENT_GOVERNMENT
 
7
OTHER
 
2

Length

Max length21
Median length12
Mean length12.24019608
Min length5

Characters and Unicode

Total characters2497
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMUNICIPALITY
2nd rowMUNICIPALITY
3rd rowMUNICIPALITY
4th rowMUNICIPALITY
5th rowMUNICIPALITY

Common Values

ValueCountFrequency (%)
MUNICIPALITY195
95.6%
DEPARTMENT_GOVERNMENT7
 
3.4%
OTHER2
 
1.0%

Length

2022-06-14T22:46:31.430739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:31.557129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
municipality195
95.6%
department_government7
 
3.4%
other2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
I585
23.4%
T218
 
8.7%
N216
 
8.7%
M209
 
8.4%
P202
 
8.1%
A202
 
8.1%
U195
 
7.8%
Y195
 
7.8%
L195
 
7.8%
C195
 
7.8%
Other values (8)85
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2490
99.7%
Connector Punctuation7
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I585
23.5%
T218
 
8.8%
N216
 
8.7%
M209
 
8.4%
P202
 
8.1%
A202
 
8.1%
U195
 
7.8%
Y195
 
7.8%
L195
 
7.8%
C195
 
7.8%
Other values (7)78
 
3.1%
Connector Punctuation
ValueCountFrequency (%)
_7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2490
99.7%
Common7
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I585
23.5%
T218
 
8.8%
N216
 
8.7%
M209
 
8.4%
P202
 
8.1%
A202
 
8.1%
U195
 
7.8%
Y195
 
7.8%
L195
 
7.8%
C195
 
7.8%
Other values (7)78
 
3.1%
Common
ValueCountFrequency (%)
_7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I585
23.4%
T218
 
8.7%
N216
 
8.7%
M209
 
8.4%
P202
 
8.1%
A202
 
8.1%
U195
 
7.8%
Y195
 
7.8%
L195
 
7.8%
C195
 
7.8%
Other values (8)85
 
3.4%

PERFORMANCE
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
NO_DEVIATION
94 
BOTH
45 
TIME_DEVIATION
40 
COST_DEVIATION
25 

Length

Max length14
Median length12
Mean length10.87254902
Min length4

Characters and Unicode

Total characters2218
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOTH
2nd rowCOST_DEVIATION
3rd rowTIME_DEVIATION
4th rowNO_DEVIATION
5th rowNO_DEVIATION

Common Values

ValueCountFrequency (%)
NO_DEVIATION94
46.1%
BOTH45
22.1%
TIME_DEVIATION40
19.6%
COST_DEVIATION25
 
12.3%

Length

2022-06-14T22:46:31.690585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-14T22:46:31.812801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no_deviation94
46.1%
both45
22.1%
time_deviation40
19.6%
cost_deviation25
 
12.3%

Most occurring characters

ValueCountFrequency (%)
I358
16.1%
O323
14.6%
T269
12.1%
N253
11.4%
E199
9.0%
_159
7.2%
D159
7.2%
V159
7.2%
A159
7.2%
B45
 
2.0%
Other values (4)135
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2059
92.8%
Connector Punctuation159
 
7.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I358
17.4%
O323
15.7%
T269
13.1%
N253
12.3%
E199
9.7%
D159
7.7%
V159
7.7%
A159
7.7%
B45
 
2.2%
H45
 
2.2%
Other values (3)90
 
4.4%
Connector Punctuation
ValueCountFrequency (%)
_159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2059
92.8%
Common159
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
I358
17.4%
O323
15.7%
T269
13.1%
N253
12.3%
E199
9.7%
D159
7.7%
V159
7.7%
A159
7.7%
B45
 
2.2%
H45
 
2.2%
Other values (3)90
 
4.4%
Common
ValueCountFrequency (%)
_159
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I358
16.1%
O323
14.6%
T269
12.1%
N253
11.4%
E199
9.0%
_159
7.2%
D159
7.2%
V159
7.2%
A159
7.2%
B45
 
2.0%
Other values (4)135
 
6.1%

Interactions

2022-06-14T22:46:05.267812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:42:53.636017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:42:59.830898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:06.337716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:13.946972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:22.741891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:28.983910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:38.101469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:43.240198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:48.700239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:54.790542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:01.414511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:08.476346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:16.156242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:22.973713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:29.037582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:37.542001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:43.192179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:49.699453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:56.783490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:03.530280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:10.057531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:16.087113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:21.423868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:27.414262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:33.286930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:41.028141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:48.396521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:54.344913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:59.518870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:46:05.455823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:42:53.867551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:00.026192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:06.648279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:14.168181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:23.154459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:29.410063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:38.285649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:43.436673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:48.893518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:54.975364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:01.686000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:08.659140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:16.425171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:23.195530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:29.240606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:37.749849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-06-14T22:44:08.100886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:15.392424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:22.307932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:28.704022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:37.176130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:42.836047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:49.273024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:56.420870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:03.151960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:09.506321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:15.750051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:21.083452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:27.061856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:32.952287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:40.544102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:48.052093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:54.008075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:59.187199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:46:04.921790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:46:10.218053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:42:59.461045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:06.133888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:13.783683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:22.460104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:28.798406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:37.434480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:43.077375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:48.529936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:43:54.613787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:01.234473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:08.280535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:15.805895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:22.696135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:28.869481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:37.362229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:43.008544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:49.482146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:44:56.601803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:03.346017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:09.838839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:15.922401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:21.263919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:27.240627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:33.118883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:40.711654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:48.222789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:54.174807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:45:59.351373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-14T22:46:05.098047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-06-14T22:46:32.045000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-14T22:46:32.746846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-14T22:46:33.397726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-14T22:46:33.948555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-14T22:46:34.372246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-14T22:46:10.736851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-14T22:46:12.464550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-14T22:46:12.945246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-14T22:46:13.293168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

NUMBER_OF_CONTRACTSCOST_DEVIATIONTIME_DEVIATIONTIME_STUDIES_CONTRACTADVANCED_PAYMENTESTIMATED_COSTORIGINAL_DEADLINELIQUIDITY_INDEX_BDEBT_INDEX_BINTEREST_COVERAGE_RATIO_BROE_BROA_BWORKING_CAPITALNET_EQUITYEXPERIENCE_B_VALUEK_CONTRACTING_B_VALUEPROJECT_INTENSITYPRICE_SCORETECHNICAL_SCORENATIONAL_INDUSTRY_SCOREOTHER_SCORENUMBER_BIDDERSCONTRACT_VALUEAWARD_GROWTHADDITIONAL_COSTFINAL_COSTADDITIONAL_TIMEFINAL_DEADLINETIME_SUSPENDED_PIDICONTRACTORHIGHEST SCORELOWEST_SCOREYEARIDI_CATCONTRACT_TYPEMUNICIPALITY_TYPEDEPARTMENTREGIONREGION_2OWNERPERFORMANCE
010.180.50111.00.0861.089010.00.501.000.050.021.00NaN2.140.549.570.500.200.100.201861.07-0.0018153.741014.81451350.6976.13INDIVIDUALPRICENATIONAL_INDUSTRYY_2016SATISFACTORYCONSTRUCTIONTYPE_6MAGDALENACARIBECARIBEMUNICIPALITYBOTH
120.250.0038.00.0325.269085.00.1685.000.130.11NaNNaN3.011.003.610.380.500.060.061325.26-0.001381.32406.570900.0072.85INDIVIDUALTECHNICALOTHERY_2017SATISFACTORYMAINTEINANCETYPE_6BOYACAANDINACENTRO_ORIENTEMUNICIPALITYCOST_DEVIATION
230.000.3841.00.41084.4312010.00.4010.000.150.153.003.011.071.009.040.500.400.100.0011084.430.00000.001084.43451650.0080.47COMPANYPRICEOTHERY_2017OUTSTANDINGMAINTEINANCETYPE_6ANTIOQUIAANDINAEJE_CAFETEROMUNICIPALITYTIME_DEVIATION
340.000.0047.00.0358.756025.00.504.000.550.30NaNNaN1.001.005.980.600.300.100.001358.42-0.08920.00358.420600.0047.26CONSORTIUMPRICEOTHERY_2018LOWMAINTEINANCETYPE_6TOLIMAANDINACENTRO_SURMUNICIPALITYNO_DEVIATION
450.000.0035.00.02020.6460250.00.155.000.100.100.99NaN1.000.4433.680.270.610.040.0812020.62-0.00120.002020.620601.2874.50CONSORTIUMTECHNICALNATIONAL_INDUSTRYY_2015SATISFACTORYMAINTEINANCETYPE_6BOYACAANDINACENTRO_ORIENTEMUNICIPALITYNO_DEVIATION
560.050.8336.00.0482.63903.00.105.000.100.10NaNNaN2.001.005.360.600.300.100.001482.62-0.000125.25507.88751650.5082.77INDIVIDUALPRICEOTHERY_2015OUTSTANDINGCONSTRUCTIONTYPE_6CUNDINAMARCAANDINACENTRO_ORIENTEMUNICIPALITYBOTH
670.000.0050.00.0642.91455.00.403.000.060.041.001.01.001.0014.290.550.300.150.002642.38-0.08240.00642.380450.6975.73CONSORTIUMPRICEOTHERY_2016SATISFACTORYMAINTEINANCEOTHERCORDOBACARIBECARIBEMUNICIPALITYNO_DEVIATION
780.000.5354.00.0374.43455.00.3020.000.300.20NaNNaN5.001.008.320.300.600.100.001373.06-0.36590.00373.0624692.7875.66COMPANYTECHNICALOTHERY_2016SATISFACTORYCONSTRUCTIONTYPE_6ANTIOQUIAANDINAEJE_CAFETEROMUNICIPALITYTIME_DEVIATION
890.000.1437.00.5748.04503.00.542.350.100.04NaNNaN5.000.5014.960.270.500.000.231748.01-0.00440.00748.017570.0076.89COMPANYTECHNICALNATIONAL_INDUSTRYY_2015SATISFACTORYMAINTEINANCETYPE_6BOYACAANDINACENTRO_ORIENTEMUNICIPALITYTIME_DEVIATION
9100.330.5635.00.0371.999012.00.3010.000.120.120.81NaN2.021.004.130.500.300.000.201371.990.0000123.28495.27501404.2975.17INDIVIDUALPRICENATIONAL_INDUSTRYY_2016SATISFACTORYCONSTRUCTIONTYPE_6SANTANDERANDINACENTRO_ORIENTEMUNICIPALITYBOTH

Last rows

NUMBER_OF_CONTRACTSCOST_DEVIATIONTIME_DEVIATIONTIME_STUDIES_CONTRACTADVANCED_PAYMENTESTIMATED_COSTORIGINAL_DEADLINELIQUIDITY_INDEX_BDEBT_INDEX_BINTEREST_COVERAGE_RATIO_BROE_BROA_BWORKING_CAPITALNET_EQUITYEXPERIENCE_B_VALUEK_CONTRACTING_B_VALUEPROJECT_INTENSITYPRICE_SCORETECHNICAL_SCORENATIONAL_INDUSTRY_SCOREOTHER_SCORENUMBER_BIDDERSCONTRACT_VALUEAWARD_GROWTHADDITIONAL_COSTFINAL_COSTADDITIONAL_TIMEFINAL_DEADLINETIME_SUSPENDED_PIDICONTRACTORHIGHEST SCORELOWEST_SCOREYEARIDI_CATCONTRACT_TYPEMUNICIPALITY_TYPEDEPARTMENTREGIONREGION_2OWNERPERFORMANCE
1941950.0000000.00000042.00.0458.82490412090.000.1510.00.100.100.6102550.5000005.001.03.8235410.350.5500.100.0001458.748308-0.0166940.000000458.74830801200.00000052.7COMPANYTECHNICALOTHERY_2019LOWMAINTEINANCETYPE_6TOLIMAANDINACENTRO_SURMUNICIPALITYNO_DEVIATION
1951960.0000000.00000039.00.0504.971863901.500.701.00.000.00NaNNaN0.501.05.6107980.500.4000.090.0101504.567792-0.0800180.000000504.5677920900.00000044.9INDIVIDUALPRICEOTHERY_2019LOWCONSTRUCTIONOTHERNARINOPACIFICAPACIFICOMUNICIPALITYNO_DEVIATION
1961970.4284490.00000060.00.0845.29220527010.000.2080.00.150.150.4285710.0012863.001.03.1307120.300.5900.100.0102845.2922050.000000362.1648661207.45707102700.00000054.1COMPANYTECHNICALOTHERY_2019LOWMAINTEINANCETYPE_6ANTIOQUIAANDINAEJE_CAFETEROMUNICIPALITYCOST_DEVIATION
1971980.2000000.66666739.00.01324.301537901.000.701.00.250.200.100000NaN0.750.714.7144620.700.1900.100.010111294.883879-2.221372258.9767751553.860654601500.00000075.3CONSORTIUMPRICEOTHERY_2019SATISFACTORYCONSTRUCTIONTYPE_6ANTIOQUIAANDINAEJE_CAFETEROMUNICIPALITYBOTH
1981990.0000000.00000042.00.05378.1640471804.200.2533.00.090.080.7000000.8000001.00NaN29.8786890.000.6000.200.20015377.178864-0.0183180.0000005377.17886401800.00000048.1COMPANYTECHNICALOTHERY_2019LOWMAINTEINANCETYPE_6ANTIOQUIAANDINAEJE_CAFETEROMUNICIPALITYNO_DEVIATION
1992000.0000000.33333388.00.0538.0412781200.800.205.00.150.132.0000003.0000003.001.04.4836770.700.1920.100.0081538.0412780.0000000.000000538.041278401600.00000050.1CONSORTIUMPRICEOTHERY_2019LOWMAINTEINANCETYPE_6CUNDINAMARCAANDINACENTRO_ORIENTEMUNICIPALITYTIME_DEVIATION
2002010.0000000.00000054.00.5603.7801471807.480.4915.20.180.16NaNNaN6.001.03.3543340.500.2500.100.1501603.728818-0.0085010.000000603.72881801800.00000045.8CONSORTIUMPRICENATIONAL_INDUSTRYY_2019LOWMAINTEINANCETYPE_6CUNDINAMARCAANDINACENTRO_ORIENTEMUNICIPALITYNO_DEVIATION
2012020.0000000.50000042.00.0594.266073901.000.701.00.000.000.300000NaN0.751.06.6029560.700.1900.100.01016577.377012-2.8420030.000000577.377012451350.00000050.8CONSORTIUMPRICEOTHERY_2019LOWMAINTEINANCEOTHERBOYACAANDINACENTRO_ORIENTEMUNICIPALITYTIME_DEVIATION
2022030.0000000.00000043.00.4507.8470481201.500.701.00.000.00NaNNaN4.00NaN4.2320590.500.4000.090.0102507.8470480.0000000.000000507.84704801200.37500044.9INDIVIDUALPRICEOTHERY_2019LOWCONSTRUCTIONOTHERNARINOPACIFICAPACIFICOMUNICIPALITYNO_DEVIATION
2032040.0643280.12500016.00.0393.65668812020.000.3626.00.320.13NaNNaN2.001.03.2804720.400.4000.100.1001391.834323-0.46293325.206098417.040421151350.30833352.0COMPANYOTHEROTHERY_2019LOWMAINTEINANCETYPE_6BOYACAANDINACENTRO_ORIENTEMUNICIPALITYBOTH